A. Pignalberi, C. Cesaroni, M. Pietrella, M. Pezzopane, L. Spogli, C. Marcocci, E. Pica
{"title":"Ionospheric Nowcasting Over Italy Through Data Assimilation: A Synergy Between IRI UP and IONORING","authors":"A. Pignalberi, C. Cesaroni, M. Pietrella, M. Pezzopane, L. Spogli, C. Marcocci, E. Pica","doi":"10.1029/2023sw003838","DOIUrl":null,"url":null,"abstract":"An accurate modeling of the ionosphere electron density is pivotal to guarantee the effective operation of communication and navigation systems, particularly during Space Weather events. Despite the crucial contribution of empirical models like the International Reference Ionosphere (IRI), their limitations in predicting ionospheric variability, especially under geomagnetically disturbed conditions, are acknowledged. The solution proposed in this work involves integrating real‐time, spatially distributed ionospheric measurements into climatological models through data assimilation. To enhance our predictive capabilities, we present an upgrade of the IRI UP data‐assimilation method, incorporating real‐time vertical total electron content (vTEC) maps from the IONORING algorithm for nowcasting ionospheric conditions over Italy. This approach involves updating the IRI F2‐layer peak electron density description through ionospheric indices, to finally produce real‐time maps over Italy of the ordinary critical frequency of the F2‐layer, foF2, which is crucial for radio‐propagation applications. The IRI UP–IONORING method performance has been evaluated against different climatological and nowcasting models, and under different Space Weather conditions, by showing promising outcomes which encourages its inclusion in the portfolio of ionospheric real‐time products available over Italy. The validation analysis highlighted also what are the current limitations of the IRI UP–IONORING method, particularly during nighttime for severely disturbed conditions, suggesting avenues for future enhancements.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"82 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Space Weather","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1029/2023sw003838","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
An accurate modeling of the ionosphere electron density is pivotal to guarantee the effective operation of communication and navigation systems, particularly during Space Weather events. Despite the crucial contribution of empirical models like the International Reference Ionosphere (IRI), their limitations in predicting ionospheric variability, especially under geomagnetically disturbed conditions, are acknowledged. The solution proposed in this work involves integrating real‐time, spatially distributed ionospheric measurements into climatological models through data assimilation. To enhance our predictive capabilities, we present an upgrade of the IRI UP data‐assimilation method, incorporating real‐time vertical total electron content (vTEC) maps from the IONORING algorithm for nowcasting ionospheric conditions over Italy. This approach involves updating the IRI F2‐layer peak electron density description through ionospheric indices, to finally produce real‐time maps over Italy of the ordinary critical frequency of the F2‐layer, foF2, which is crucial for radio‐propagation applications. The IRI UP–IONORING method performance has been evaluated against different climatological and nowcasting models, and under different Space Weather conditions, by showing promising outcomes which encourages its inclusion in the portfolio of ionospheric real‐time products available over Italy. The validation analysis highlighted also what are the current limitations of the IRI UP–IONORING method, particularly during nighttime for severely disturbed conditions, suggesting avenues for future enhancements.
电离层电子密度的精确建模对于保证通信和导航系统的有效运行至关重要,特别是在空间天气事件期间。尽管国际参考电离层(IRI)等经验模型做出了重要贡献,但它们在预测电离层变化,特别是地磁干扰条件下的电离层变化方面的局限性是公认的。这项工作提出的解决方案涉及通过数据同化将实时、空间分布式电离层测量数据整合到气候学模型中。为了提高预测能力,我们对 IRI UP 数据同化方法进行了升级,将 IONORING 算法提供的实时垂直电子总含量(vTEC)图纳入其中,用于预报意大利上空的电离层状况。这种方法包括通过电离层指数更新 IRI F2 层峰值电子密度描述,最终生成意大利上空 F2 层普通临界频率 foF2 的实时地图,这对无线电传播应用至关重要。根据不同的气候和预报模型,在不同的空间天气条件下,对 IRI UP-IONORING 方法的性能进行了评估,显示出良好的结果,鼓励将其纳入意大利电离层实时产品组合。验证分析还强调了 IRI UP-IONORING 方法目前存在的局限性,特别是在夜间严重干扰条件下,为今后的改进提出了建议。